Reducing risk in your manufacturing footprint

Flexibility within and among locations can help companies respond to changing conditions.

April 2009| byEric Lamarre, Martin Pergler, and Gregory Vainberg

Manufacturers of all types seek the same holy grail: the strategy that delivers products at the lowest possible total landed cost. In search of that goal, over the past few years companies all over the world have relocated facilities, outsourced production to low-cost countries, invested in automation, consolidated plants, or fundamentally redefined relationships with suppliers.

Establishing the cheapest manufacturing footprint becomes infinitely more elusive when basic assumptions change fast and furiously, as they have in the past year. Redesigning the footprint can be the biggest and most important transformation a manufacturer can undertake. Yet too many managers choose the footprint by using only a single set of future cost and demand assumptions. Any manufacturing footprint exposes companies to risks, such as changes in local and global demand, currency exchange rates, labor and transportation costs, or even trade regulation. A wrong bet can transform what should be a competitive advantage into a mess of underutilized or high-cost assets.

In our experience, the missing ingredient in many manufacturing-strategy decisions is a careful consideration of the value of flexibility. Companies that build it into their manufacturing presence can respond more nimbly to changing conditions and outperform competitors with less flexible footprints. As the current economic turmoil illustrates, the greater the level of uncertainty, the greater the value of flexibility. This is not surprising; real-options theory (see sidebar, “What are real options?”) maintains that flexibility is more important when volatility is more intense. To capture this value and gain the best position for responding to future economic changes, all companies should integrate flexibility into their manufacturing-footprint or sourcing decisions.

Sidebar

What are real options?

Real-options theory has its roots in the model developed for financial options by Fischer Black and Myron Scholes and later modified by Robert Merton. A company can use similar models to value business or capital decisions that give it the right, but not the obligation, to undertake a specific action later, depending on how circumstances evolve. These real options include expanding or shutting down a factory or selling or acquiring an asset.

The idea of real options is very intuitive—a small investment now “just in case” can pay off significantly, especially if the level of uncertainty is large. Managers often treat standard real-options calculations with suspicion, however, since the mathematical analysis requires simplifying assumptions about exactly how flexibility would be captured. Our approach to managing a company’s manufacturing footprint is an application of the real-options idea but grounded in very concrete analysis of the operational decisions managers must make to capture flexibility.

Sources of flexibility

Some sectors understand the importance of flexibility. Peak-demand power plants, for example, are inherently quite costly but play an important role in the market because they can quickly be brought online for short periods when high energy demand drives up electricity prices. Petroleum refineries can alter their product mix weekly (or even daily), basing these changes on the relative prices of different distillates, product inventories, and the price and availability of crude oil.

Industrial examples are less common. Honda’s East Liberty, Ohio, plant can switch in minutes from producing the Civic, an economical passenger car, to the crossover sport utility CR-V. The Southeast Asian plant of a construction-equipment manufacturer was designed to make two different products on the same assembly line. Every month, the plant can switch production schedules to meet Chinese, Southeast Asian, and Indian demand for either product.

Many manufacturers, however, fail to assess the flexibility and resilience to risk of their manufacturing footprint options, much less invest in flexibility to make themselves more responsive. Flexibility in a company’s manufacturing footprint may take a number of forms: for instance, the ability to adjust overall production volumes up or down efficiently, depending on demand and profitability; to change the production mix among different products or models; or to adapt the timing of production by shortening lead times or committing the company to production volumes later than competitors do. A flexible footprint can also manifest itself in a company’s dispatch optimization—its ability to adjust the country or facility from which products or parts are sourced in order to minimize the total landed cost at the desired destination, given actual market conditions.

When companies build in these sources of strategic flexibility, they can respond tactically to risks such as changes in local demand, currency levels, labor rates, tariffs, taxes, and transportation costs. Toyota Motor, for instance, has increasingly placed its manufacturing plants around the world for maximum responsiveness to local market conditions—starting with its NUMMI joint venture with GM in California during the 1980s. By 2004, Toyota realized that these efforts had significantly reduced its overall risk exposure (currency risk, in particular) by matching the currencies of local costs and revenues.

Valuing and liberating flexibility

We find it useful to distinguish between two types of flexibility. The first is flexibility within the four walls of any given manufacturing facility. Plant flexibility might be manifested, for example, when a manufacturing manager decides whether to change production levels at a given factory or a purchasing manager decides which supplier to use. While the decision itself is simple, increasing an individual plant’s flexibility is often fairly expensive: for example, it can mean adding capacity, adopting more expensive tooling to facilitate mix changes, or negotiating more flexible labor or supplier agreements.

The second type of flexibility, at the level of a company’s network of plants, calls for integrating information from around the enterprise to make networkwide optimization decisions. One US manufacturer, for example, expected to serve customers in North America from plants in North and Central America and customers in Europe from European plants. When demand increased in the United States, however, falling shipping costs, a stronger US dollar, and capacity constraints made it worthwhile for the company to ramp up European production as much as possible and to ship products across the Atlantic. The specific balance of production and transportation costs in each of the three plants required a holistic view of the whole network (and, in this case, a new shipping flow).

While the decisions (and the information requirements) for this second kind of flexibility are more complex, increasing it may be less expensive than building flexibility within an individual plant. Indeed, the more complex the footprint, the more likely that some sort of hidden network flexibility is readily available. A company with a multinational footprint, for example, might have significant potential flexibility to adjust production levels and shipping flows between different regions in response to changing local economic conditions. It could realize this possibility only if it had sufficiently transparent sources of information and made managers responsible for exploiting them.

Consider the example of a heavy-equipment manufacturer exploring potential new footprints to reduce its cost base and maintain its competitive position against low-cost entrants. The leading new footprint option—to build new plants in developing countries and to reallocate the product mix and capacity of existing plants—was clearly more cost-effective given the expected evolution of demand and costs. Nonetheless, increased currency exposure and transportation flows would significantly raise the company’s overall level of risk. Once managers incorporated flexibility into their analysis, however, the new footprint option became significantly more attractive. They then realized that a more geographically diversified footprint would enable them to respond more easily to unexpected changes in costs or demand—an ability that lowered both the expected unit cash cost and the uncertainty. In effect, the new footprint provided very concrete and valuable real options. Capturing them required an incremental increase in investment, but the lower unit costs and greater flexibility were clearly worthwhile.

In this instance, flexibility improved the case for what was already a worthwhile new footprint investment. But suppose that had been one of two possible new footprint options. Basing the decision between them solely on expected costs, without considering flexibility—as many companies do—would probably have made the company choose the costlier option (exhibit).

Exhibit

What should companies do?

The example above shows that if companies take risk and flexibility into account when they make manufacturing-footprint decisions, they can make better ones, particularly under high uncertainty. To capitalize on that opportunity, companies must take several steps.

Phase 1: Modeling landed costs

The starting point for exploring manufacturing-footprint options is a detailed landed-cost model for all options under consideration. To understand the cost of manufacturing and delivering a unit of each product to each destination, managers must also understand the marginal costs of producing and shipping more or fewer units. This is trickier than it seems, since the financial systems of many companies tend to track the required factor cost items only on a plant-by-plant level. Cutting the numbers with sufficient granularity will require a combined effort involving the finance function and the shop floor, as well as the design team for the options being considered.

Phase 2: Exploring risk and flexibility

In this phase, managers need to assess the risks affecting costs and demand. Some risks can be assessed fairly easily: for example, local GDP growth may influence local industry demand directly, and the evolution of local labor rates may feed straight into factor costs. Others risks are a bit more challenging, since they affect more than one element of the cost base. Energy prices, for instance, typically appear not only as a direct manufacturing cost but also as a contributor to transportation costs—both in shipping products to end-user markets and in shipping modules or parts from factories or suppliers to assembly. Currency risk, which can be particularly difficult to assess accurately, is often a critically important consideration, as well.

Besides understanding the risks, managers need to understand the sources of flexibility in each footprint option. Which combinations of production volume, mix, dispatch, and timing are available for each? How is flexibility constrained—for example, by maximum production capacity or transportation bottlenecks. What can be done within the four walls of an individual plant and what at the level of the plant network? The heavy-equipment manufacturer discussed above built its model for over 60 products, a dozen geographic regions, and 50 partially correlated risk factors.

In our experience, the principal difficulty in this phase is tracking the impact of different risk factors and flexibility decisions through all of the line items. Effects can be hidden—for instance, increases in the price of energy affect costs not only for manufacturing but also for transportation, as well as supplier costs that may be passed on through escalation clauses.

Phase 3: Quantifying the trade-offs

To make the cost and flexibility trade-offs for different footprint options, companies must combine the risks and sources of flexibility with base-case demand predictions and landed-cost models. A variety of analytical techniques are available. If just a handful of largely independent uncertainties really matter, managers may need only a simple computation of the economics of each footprint option in a small set of scenarios. When the number of variables is larger and their relationships are more complicated, probabilistic modeling often makes more sense, as it did for the heavy-equipment manufacturer. In such cases, it’s essential to program the model with rules for the managerial flexibility each footprint option allows—rules such as “if demand exceeds capacity, start a third shift” or “ship units from Mexico if they turn out to be cheaper than units from Indonesia.” The heavy-equipment manufacturer ran several thousand Monte Carlo scenarios on its model, recalculating capacity and dispatch flows according to economic conditions.

Both scenario analysis and probabilistic modeling are only as good as the quality of a company’s understanding of its key assumptions. What’s needed is a combination of what-if analysis, external data, expert predictions, stress testing, and extrapolation from the available historical data.

Phase 4: Making the choice and improving value

The calculations described above often clarify which footprint choice is best under a broad range of situations. Some options might provide the lowest landed costs even in the face of broad swings in economic conditions. In other cases, one option beats out others only because greater flexibility helps a company adapt more successfully to certain kinds of change, such as increased competition or regional fluctuations in demand. A footprint that seems more expensive or that requires a higher level of investment might be worthwhile for the extra flexibility.

Debating these possibilities will probably generate additional ideas to enhance a company’s flexibility. Managers of a liquid-natural-gas (LNG) supplier, for instance, were considering whether efforts to acquire or develop a number of gas fields, pipelines, and LNG terminals would provide greater flexibility in responding to regional imbalances in demand. Analysis confirmed that they would do so but also showed that the company could capture extra value by improving its dispatch capabilities to change network flows in its whole portfolio of assets. The company believed that this additional network flexibility, requiring only new managerial skills and information systems but no physical modifications, would have an economic value easily exceeding the additional investment.

Finally, it’s worth stressing that the work doesn’t stop with adjusting the network. Managers face a constant stream of decisions, such as investing in modernization, adding new capacity, and introducing new products. That’s in addition to more day-to-day decisions in production planning to capture the value of—and preserve—the network’s flexibility. Making such decisions typically requires the use of ongoing coordination mechanisms across plants, appropriate steps to measure and plan capacity, and the adjustment of metrics that emphasize the value of the whole enterprise, as opposed to individual plants. Such activities, worthwhile in themselves, are doubly important if network flexibility is a key part of a new footprint’s value.

About the authors

Eric Lamarre is a director in McKinsey’s Montréal office, where Martin Pergler and Gregory Vainberg are consultants.

The authors would like to thank Vijai Raghavan for his contributions to this article.

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